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<div class="textblock">Here are the classes, structs, unions and interfaces with brief descriptions:</div><div class="directory">
<div class="levels">[detail level <span onclick="javascript:dynsection.toggleLevel(1);">1</span><span onclick="javascript:dynsection.toggleLevel(2);">2</span><span onclick="javascript:dynsection.toggleLevel(3);">3</span><span onclick="javascript:dynsection.toggleLevel(4);">4</span>]</div><table class="directory">
<tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_0_" class="arrow" onclick="dynsection.toggleFolder('0_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><b>nz</b></td><td class="desc"></td></tr>
<tr id="row_0_0_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_0_" class="arrow" onclick="dynsection.toggleFolder('0_0_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacenz_1_1cu_strm.html" target="_self">cuStrm</a></td><td class="desc">Provides core components for CUDA stream and event lifecycle management in GPU computing environments </td></tr>
<tr id="row_0_0_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1cu_strm_1_1_event_pool.html" target="_self">EventPool</a></td><td class="desc">Internal event management system for CUDA stream synchronization (Part of <a class="el" href="classnz_1_1cu_strm_1_1_stream_manager.html" title="Centralized CUDA stream and resource management system with automatic dependency tracking.">StreamManager</a>) </td></tr>
<tr id="row_0_0_1_" class="odd"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1cu_strm_1_1_stream_manager.html" target="_self">StreamManager</a></td><td class="desc">Centralized CUDA stream and resource management system with automatic dependency tracking </td></tr>
<tr id="row_0_1_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_1_" class="arrow" onclick="dynsection.toggleFolder('0_1_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacenz_1_1data.html" target="_self">data</a></td><td class="desc">Contains data structures and utilities for tensor operations in machine learning workflows </td></tr>
<tr id="row_0_1_0_" class="odd"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1data_1_1_dimension.html" target="_self">Dimension</a></td><td class="desc">Represents a multi - dimensional shape, typically used in deep learning for tensor dimensions </td></tr>
<tr id="row_0_1_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1data_1_1_mapped_tensor.html" target="_self">MappedTensor</a></td><td class="desc">A class for representing multidimensional arrays in CUDA zero-copy memory, providing host-accessible container-like interfaces </td></tr>
<tr id="row_0_1_2_" class="odd"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1data_1_1_tensor.html" target="_self">Tensor</a></td><td class="desc">A class for representing and manipulating multidimensional arrays (tensors) in GPU memory </td></tr>
<tr id="row_0_2_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_2_" class="arrow" onclick="dynsection.toggleFolder('0_2_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacenz_1_1graph.html" target="_self">graph</a></td><td class="desc">Contains classes and functions for managing and executing computation graphs in deep learning workflows </td></tr>
<tr id="row_0_2_0_" class="odd"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1graph_1_1_compute_graph.html" target="_self">ComputeGraph</a></td><td class="desc">Represents a computational graph, which manages nodes and the computation flow </td></tr>
<tr id="row_0_3_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_3_" class="arrow" onclick="dynsection.toggleFolder('0_3_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacenz_1_1nodes.html" target="_self">nodes</a></td><td class="desc">Contains classes and functionality for nodes in a neural network or computational graph </td></tr>
<tr id="row_0_3_0_" class="odd"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_3_0_" class="arrow" onclick="dynsection.toggleFolder('0_3_0_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacenz_1_1nodes_1_1calc.html" target="_self">calc</a></td><td class="desc">Contains classes and functionality for computation nodes in a neural network or computational graph </td></tr>
<tr id="row_0_3_0_0_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_add_node.html" target="_self">AddNode</a></td><td class="desc">Represents a node that performs element-wise addition between two input tensors </td></tr>
<tr id="row_0_3_0_1_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_average_pooling_node.html" target="_self">AveragePoolingNode</a></td><td class="desc">Implements average pooling operation for spatial downsampling in neural networks </td></tr>
<tr id="row_0_3_0_2_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_col2_img_node.html" target="_self">Col2ImgNode</a></td><td class="desc">Reconstructs spatial tensors from column matrices generated by im2col transformation </td></tr>
<tr id="row_0_3_0_3_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_e_l_u_node.html" target="_self">ELUNode</a></td><td class="desc">Represents an Exponential Linear Unit (ELU) activation function node in a computational graph </td></tr>
<tr id="row_0_3_0_4_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_expand_node.html" target="_self">ExpandNode</a></td><td class="desc">Expands tensors with batch size 1 to arbitrary batch dimensions through data replication </td></tr>
<tr id="row_0_3_0_5_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_global_avg_pool_node.html" target="_self">GlobalAvgPoolNode</a></td><td class="desc">Performs global average pooling operation across spatial dimensions of input tensor </td></tr>
<tr id="row_0_3_0_6_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_global_max_pool_node.html" target="_self">GlobalMaxPoolNode</a></td><td class="desc">Performs global max pooling operation across spatial dimensions of input tensor </td></tr>
<tr id="row_0_3_0_7_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_hard_sigmoid_node.html" target="_self">HardSigmoidNode</a></td><td class="desc">Represents a Hard Sigmoid activation function node in a computational graph </td></tr>
<tr id="row_0_3_0_8_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_hard_swish_node.html" target="_self">HardSwishNode</a></td><td class="desc">Represents a Hard Swish activation function node in a computational graph </td></tr>
<tr id="row_0_3_0_9_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_img2_col_node.html" target="_self">Img2ColNode</a></td><td class="desc">Implements im2col transformation for efficient convolution operations in neural networks </td></tr>
<tr id="row_0_3_0_10_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_leaky_re_l_u_node.html" target="_self">LeakyReLUNode</a></td><td class="desc">Represents a Leaky Rectified Linear Unit (LeakyReLU) activation function node in a computational graph </td></tr>
<tr id="row_0_3_0_11_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_mat_mul_node.html" target="_self">MatMulNode</a></td><td class="desc">Represents a matrix multiplication operation node in a computational graph </td></tr>
<tr id="row_0_3_0_12_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_max_pooling_node.html" target="_self">MaxPoolingNode</a></td><td class="desc">Implements max pooling operation for spatial downsampling with feature preservation </td></tr>
<tr id="row_0_3_0_13_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_re_l_u_node.html" target="_self">ReLUNode</a></td><td class="desc">Represents a Rectified Linear Unit (ReLU) operation node in a computational graph </td></tr>
<tr id="row_0_3_0_14_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_reshape_node.html" target="_self">ReshapeNode</a></td><td class="desc">Implements tensor shape transformation within a neural network computational graph </td></tr>
<tr id="row_0_3_0_15_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_scalar_add_node.html" target="_self">ScalarAddNode</a></td><td class="desc">Represents a scalar addition operation node in a computational graph </td></tr>
<tr id="row_0_3_0_16_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_scalar_div_node.html" target="_self">ScalarDivNode</a></td><td class="desc">Represents a scalar division operation node in a computational graph </td></tr>
<tr id="row_0_3_0_17_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_scalar_mul_node.html" target="_self">ScalarMulNode</a></td><td class="desc">Represents a scalar multiplication operation node in a computational graph </td></tr>
<tr id="row_0_3_0_18_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_scalar_sub_node.html" target="_self">ScalarSubNode</a></td><td class="desc">Represents a scalar subtraction operation node in a computational graph </td></tr>
<tr id="row_0_3_0_19_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_sigmoid_node.html" target="_self">SigmoidNode</a></td><td class="desc">Represents a Sigmoid activation function node in a computational graph </td></tr>
<tr id="row_0_3_0_20_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_softmax_node.html" target="_self">SoftmaxNode</a></td><td class="desc">Implements the Softmax activation function as a node in a neural network computational graph </td></tr>
<tr id="row_0_3_0_21_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_sub_node.html" target="_self">SubNode</a></td><td class="desc">Represents a subtraction operation node in a computational graph </td></tr>
<tr id="row_0_3_0_22_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_swish_node.html" target="_self">SwishNode</a></td><td class="desc">Represents a Swish activation function node in a computational graph </td></tr>
<tr id="row_0_3_0_23_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1calc_1_1_tanh_node.html" target="_self">TanhNode</a></td><td class="desc">Represents a hyperbolic tangent (tanh) activation function node in a computational graph </td></tr>
<tr id="row_0_3_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_3_1_" class="arrow" onclick="dynsection.toggleFolder('0_3_1_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacenz_1_1nodes_1_1io.html" target="_self">io</a></td><td class="desc">This namespace contains standard nodes used in computational graphs for neural networks </td></tr>
<tr id="row_0_3_1_0_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1io_1_1_input_node.html" target="_self">InputNode</a></td><td class="desc">Represents an input node in a computational graph </td></tr>
<tr id="row_0_3_1_1_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1io_1_1_output_node.html" target="_self">OutputNode</a></td><td class="desc">Base class for loss function nodes in a computational graph </td></tr>
<tr id="row_0_3_2_" class="odd"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_3_2_" class="arrow" onclick="dynsection.toggleFolder('0_3_2_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacenz_1_1nodes_1_1loss.html" target="_self">loss</a></td><td class="desc">Contains loss function nodes for computing various loss metrics in a machine learning model </td></tr>
<tr id="row_0_3_2_0_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1loss_1_1_binary_cross_entropy_node.html" target="_self">BinaryCrossEntropyNode</a></td><td class="desc">Represents the Binary Cross-Entropy (BCE) loss function node in a computational graph </td></tr>
<tr id="row_0_3_2_1_" class="odd"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1loss_1_1_mean_squared_error_node.html" target="_self">MeanSquaredErrorNode</a></td><td class="desc">Represents the Mean Squared Error (MSE) loss function node in a computational graph </td></tr>
<tr id="row_0_3_3_" class="even"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1nodes_1_1_node.html" target="_self">Node</a></td><td class="desc">Base class for nodes in a neural network or computational graph </td></tr>
<tr id="row_0_4_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_4_" class="arrow" onclick="dynsection.toggleFolder('0_4_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacenz_1_1opt.html" target="_self">opt</a></td><td class="desc">Contains optimization algorithms for training deep learning models </td></tr>
<tr id="row_0_4_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1opt_1_1_ada_delta.html" target="_self">AdaDelta</a></td><td class="desc"><a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> optimizer for deep learning models </td></tr>
<tr id="row_0_4_1_" class="odd"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1opt_1_1_ada_grad.html" target="_self">AdaGrad</a></td><td class="desc"><a class="el" href="classnz_1_1opt_1_1_ada_grad.html" title="AdaGrad optimizer for deep learning models.">AdaGrad</a> optimizer for deep learning models </td></tr>
<tr id="row_0_4_2_" class="even"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1opt_1_1_adam.html" target="_self">Adam</a></td><td class="desc"><a class="el" href="classnz_1_1opt_1_1_adam.html" title="Adam optimizer for deep learning models.">Adam</a> optimizer for deep learning models </td></tr>
<tr id="row_0_4_3_" class="odd"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1opt_1_1_momentum.html" target="_self">Momentum</a></td><td class="desc"><a class="el" href="classnz_1_1opt_1_1_momentum.html" title="Momentum optimizer for deep learning models.">Momentum</a> optimizer for deep learning models </td></tr>
<tr id="row_0_4_4_" class="even"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1opt_1_1_n_adam.html" target="_self">NAdam</a></td><td class="desc"><a class="el" href="classnz_1_1opt_1_1_n_adam.html" title="NAdam optimizer for deep learning models.">NAdam</a> optimizer for deep learning models </td></tr>
<tr id="row_0_4_5_" class="odd"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1opt_1_1_optimizer.html" target="_self">Optimizer</a></td><td class="desc">Base class for optimization algorithms in deep learning </td></tr>
<tr id="row_0_4_6_" class="even"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1opt_1_1_r_m_sprop.html" target="_self">RMSprop</a></td><td class="desc"><a class="el" href="classnz_1_1opt_1_1_r_m_sprop.html" title="RMSprop optimizer for deep learning models.">RMSprop</a> optimizer for deep learning models </td></tr>
<tr id="row_0_4_7_" class="odd"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1opt_1_1_s_g_d.html" target="_self">SGD</a></td><td class="desc">Stochastic Gradient Descent (<a class="el" href="classnz_1_1opt_1_1_s_g_d.html" title="Stochastic Gradient Descent (SGD) optimizer for deep learning models.">SGD</a>) optimizer for deep learning models </td></tr>
<tr id="row_0_5_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1_cuda_exception.html" target="_self">CudaException</a></td><td class="desc">A final class that represents CUDA exceptions, inheriting from std::runtime_error </td></tr>
<tr id="row_0_6_" class="odd"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classnz_1_1_model.html" target="_self">Model</a></td><td class="desc">Base class for constructing neural network models with automatic computation graph management </td></tr>
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